一个基础变压器利用全夜,多通道睡眠研究数据准确地分类睡眠阶段
Benjamin Fox1, Joy Jiang1, Sajila Wickramaratne2
1The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
medRxiv : the preprint server for health sciences
|August 16, 2024
概括
一个新的基础变压器模型,PFTSleep,通过使用全夜,多通道多睡眠图 (PSG) 数据来增强睡眠阶段的分类. 这种人工智能 (AI) 方法在准确识别睡眠阶段方面优于现有的方法.
科学领域:
- 人工智能的人工智能
- 睡眠医学 睡眠医学
- 生物医学工程 生物医学工程
背景情况:
- 准确的睡眠阶段分类对于诊断睡眠障碍至关重要.
- 现有的人工智能 (AI) 方法通常依赖于有限的数据或更少的信号.
- 基础变压器模型为处理复杂的纵向生物数据提供了潜力.
研究的目的:
- 通过使用8小时多通道多睡眠图 (PSG) 数据,评估用于睡眠阶段分类的基础变压器模型 (PFTSleep).
- 将PFTSleep的性能与现有的AI睡眠阶段分类方法进行比较.
主要方法:
- 训练了一个自主监督的基础变压器 (PFTSleep) 在8小时,125赫兹的PSG数据上,有7个信号 (大脑,运动,心脏,氧气,呼吸系统).
- 使用PFTSleep编码来训练单独的睡眠阶段分类模型,而不改变基础变压器重量.
- 在睡眠心脏健康研究 (SHHS) 和动脉样硬化多民族研究 (MESA) 数据集上验证和测试了模型.
主要成果:
- 在SHHS验证集中,PFTSleep在睡眠阶段实现了高的接收器运行特征曲线 (AUROC) 下面区域 (0.95-0.99) 和精度回忆曲线 (AUPRC) 下面区域 (0.40-0.82) 的得分.
- 在MESA测试组中,AUROC得分在0.77-0.96之间,AUPRC得分在0.16-0.65.5之间.
- 与最长的上下文窗口最先进的模型相比,PFTSleep显示了较好的宏观评估得分,包括3.7%的灵敏度增加.
结论:
- 基础变压器模型利用全夜,多通道的PSG数据显著改善睡眠阶段的分类.
- PFTSleep的方法为人工智能驱动的睡眠分析提供了更强大,更准确的方法.
- 这一进步对改善睡眠障碍的诊断和管理有影响.
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